Merge branch 'disable_initialization'

from modules.paths import script_path, models_path

def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None) -> list:

def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list:

    """

    A one-and done loader to try finding the desired models in specified directories.

                    full_path = file

                    if os.path.isdir(full_path):

                        continue

                    if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]):

                        continue

                    if len(ext_filter) != 0:

                        model_name, extension = os.path.splitext(file)

                        if extension not in ext_filter:

import ldm.modules.encoders.modules

import open_clip

import torch

import transformers.utils.hub

class DisableInitialization:

    """

    When an object of this class enters a `with` block, it starts:

    - preventing torch's layer initialization functions from working

    - changes CLIP and OpenCLIP to not download model weights

    - changes CLIP to not make requests to check if there is a new version of a file you already have

    When it leaves the block, it reverts everything to how it was before.

    Use it like this:

    ```

    with DisableInitialization():

        do_things()

    ```

    """

    def __enter__(self):

        def do_nothing(*args, **kwargs):

            pass

        def create_model_and_transforms_without_pretrained(*args, pretrained=None, **kwargs):

            return self.create_model_and_transforms(*args, pretrained=None, **kwargs)

        def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):

            return self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)

        def transformers_modeling_utils_load_pretrained_model(*args, **kwargs):

            args = args[0:3] + ('/', ) + args[4:]  # resolved_archive_file; must set it to something to prevent what seems to be a bug

            return self.transformers_modeling_utils_load_pretrained_model(*args, **kwargs)

        def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):

            # this file is always 404, prevent making request

            if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json':

                raise transformers.utils.hub.EntryNotFoundError

            try:

                return original(url, *args, local_files_only=True, **kwargs)

            except Exception as e:

                return original(url, *args, local_files_only=False, **kwargs)

        def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):

            return transformers_utils_hub_get_file_from_cache(self.transformers_utils_hub_get_from_cache, url, *args, **kwargs)

        def transformers_tokenization_utils_base_cached_file(url, *args, local_files_only=False, **kwargs):

            return transformers_utils_hub_get_file_from_cache(self.transformers_tokenization_utils_base_cached_file, url, *args, **kwargs)

        def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs):

            return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs)

        self.init_kaiming_uniform = torch.nn.init.kaiming_uniform_

        self.init_no_grad_normal = torch.nn.init._no_grad_normal_

        self.init_no_grad_uniform_ = torch.nn.init._no_grad_uniform_

        self.create_model_and_transforms = open_clip.create_model_and_transforms

        self.CLIPTextModel_from_pretrained = ldm.modules.encoders.modules.CLIPTextModel.from_pretrained

        self.transformers_modeling_utils_load_pretrained_model = getattr(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', None)

        self.transformers_tokenization_utils_base_cached_file = getattr(transformers.tokenization_utils_base, 'cached_file', None)

        self.transformers_configuration_utils_cached_file = getattr(transformers.configuration_utils, 'cached_file', None)

        self.transformers_utils_hub_get_from_cache = getattr(transformers.utils.hub, 'get_from_cache', None)

        torch.nn.init.kaiming_uniform_ = do_nothing

        torch.nn.init._no_grad_normal_ = do_nothing

        torch.nn.init._no_grad_uniform_ = do_nothing

        open_clip.create_model_and_transforms = create_model_and_transforms_without_pretrained

        ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = CLIPTextModel_from_pretrained

        if self.transformers_modeling_utils_load_pretrained_model is not None:

            transformers.modeling_utils.PreTrainedModel._load_pretrained_model = transformers_modeling_utils_load_pretrained_model

        if self.transformers_tokenization_utils_base_cached_file is not None:

            transformers.tokenization_utils_base.cached_file = transformers_tokenization_utils_base_cached_file

        if self.transformers_configuration_utils_cached_file is not None:

            transformers.configuration_utils.cached_file = transformers_configuration_utils_cached_file

        if self.transformers_utils_hub_get_from_cache is not None:

            transformers.utils.hub.get_from_cache = transformers_utils_hub_get_from_cache

    def __exit__(self, exc_type, exc_val, exc_tb):

        torch.nn.init.kaiming_uniform_ = self.init_kaiming_uniform

        torch.nn.init._no_grad_normal_ = self.init_no_grad_normal

        torch.nn.init._no_grad_uniform_ = self.init_no_grad_uniform_

        open_clip.create_model_and_transforms = self.create_model_and_transforms

        ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = self.CLIPTextModel_from_pretrained

        if self.transformers_modeling_utils_load_pretrained_model is not None:

            transformers.modeling_utils.PreTrainedModel._load_pretrained_model = self.transformers_modeling_utils_load_pretrained_model

        if self.transformers_tokenization_utils_base_cached_file is not None:

            transformers.utils.hub.cached_file = self.transformers_tokenization_utils_base_cached_file

        if self.transformers_configuration_utils_cached_file is not None:

            transformers.utils.hub.cached_file = self.transformers_configuration_utils_cached_file

        if self.transformers_utils_hub_get_from_cache is not None:

            transformers.utils.hub.get_from_cache = self.transformers_utils_hub_get_from_cache

import os.path

import sys

import gc

import time

from collections import namedtuple

import torch

import re

from ldm.util import instantiate_from_config

from modules import shared, modelloader, devices, script_callbacks, sd_vae

from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors

from modules.paths import models_path

from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting

def list_models():

    checkpoints_list.clear()

    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"])

    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], ext_blacklist=[".vae.safetensors"])

    def modeltitle(path, shorthash):

        abspath = os.path.abspath(path)

    midas.api.load_model = load_model_wrapper

class Timer:

    def __init__(self):

        self.start = time.time()

    def elapsed(self):

        end = time.time()

        res = end - self.start

        self.start = end

        return res

def load_model(checkpoint_info=None):

    from modules import lowvram, sd_hijack

    checkpoint_info = checkpoint_info or select_checkpoint()

    if shared.cmd_opts.no_half:

        sd_config.model.params.unet_config.params.use_fp16 = False

    timer = Timer()

    try:

        with sd_disable_initialization.DisableInitialization():

            sd_model = instantiate_from_config(sd_config.model)

    except Exception as e:

        print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)

        sd_model = instantiate_from_config(sd_config.model)

    elapsed_create = timer.elapsed()

    load_model_weights(sd_model, checkpoint_info)

    elapsed_load_weights = timer.elapsed()

    if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:

        lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)

    else:

    script_callbacks.model_loaded_callback(sd_model)

    print("Model loaded.")

    elapsed_the_rest = timer.elapsed()

    print(f"Model loaded in {elapsed_create + elapsed_load_weights + elapsed_the_rest:.1f}s ({elapsed_create:.1f}s create model, {elapsed_load_weights:.1f}s load weights).")

    return sd_model

    sd_hijack.model_hijack.undo_hijack(sd_model)

    timer = Timer()

    try:

        load_model_weights(sd_model, checkpoint_info)

    except Exception as e:

        if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:

            sd_model.to(devices.device)

    print("Weights loaded.")

    elapsed = timer.elapsed()

    print(f"Weights loaded in {elapsed:.1f}s.")

    return sd_model